Ireneusz Bachanowicz
2763b40b60
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- Simplified the FastAPI application structure and improved error handling with middleware. - Introduced a retry decorator for asynchronous functions to enhance reliability. - Modularized the LLM initialization and prompt loading into separate functions for better maintainability. - Updated Pydantic models for Jira webhook payload and analysis flags to ensure proper validation and structure. - Implemented a structured logging configuration for better traceability and debugging. - Added comprehensive unit tests for prompt loading, response validation, and webhook handling. - Established a CI/CD pipeline with GitHub Actions for automated testing and coverage reporting. - Enhanced the prompt template for LLM analysis to include specific instructions for handling escalations.
29 lines
971 B
Python
29 lines
971 B
Python
import unittest
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from llm.chains import load_prompt_template, validate_response
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from llm.models import AnalysisFlags
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class PromptTests(unittest.TestCase):
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def test_prompt_loading(self):
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"""Test that prompt template loads correctly"""
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try:
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template = load_prompt_template()
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self.assertIsNotNone(template)
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self.assertIn("issueKey", template.input_variables)
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except Exception as e:
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self.fail(f"Prompt loading failed: {str(e)}")
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def test_response_validation(self):
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"""Test response validation logic"""
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valid_response = {
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"hasMultipleEscalations": False,
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"customerSentiment": "neutral"
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}
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invalid_response = {
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"customerSentiment": "neutral"
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}
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self.assertTrue(validate_response(valid_response))
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self.assertFalse(validate_response(invalid_response))
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if __name__ == "__main__":
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unittest.main() |